Continual Reinforcement Learning for Intelligent Agricultural Management under Climate Changes
Source abstract
Abstract
Climate change poses significant challenges to agricultural management, particularly in adapting to extreme weather conditions that impact agricultural production. Existing works with traditional Reinforcement Learning (RL) m... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Impact of AI and Big Data on Business and Society · Greenhouse Technology and Climate Control
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (35)
Show source-provided references
1. Precision agriculture—A worldwide overview10.1016/S0168-1699(02)00096-0 ↗
2. Reinforcement learning: A survey10.1613/jair.301 ↗
3. Safe reinforcement learning under temporal logic with reward design and quantum action selection10.1038/s41598-023-28582-4 ↗
4. Model-based motion planning in POMDPs with temporal logic specifications10.1080/01691864.2023.2226191 ↗
5. R. Gautron, E. J. Padrón, P. Preux, J. Bigot, O. -A. Maillard and D. Emukpere, “gym-DSSAT: A crop model turned into a reinforcement learning environment,” Sep. 27, 2022. doi: 10.48550/arXiv.2207.03270.
6. The DSSAT cropping system model10.1016/S1161-0301(02)00107-7 ↗
7. J. Wu, R. Tao, P. Zhao, N. F. Martin, and N. Hovakimyan, “Optimizing nitrogen management with deep reinforcement learning and crop simulations,” Apr. 21, 2022. doi: 10.1109/CVPRW56347.2022.00178.10.1109/CVPRW56347.2022.00178 ↗
8. Reinforcement learning control for water-efficient agricultural irrigation
9. Z. Wang, S. Xiao, J. Li, and J. Wang, “Learning-based agricultural management in partially observable environments subject to climate variability,” Jan. 02, 2024. doi: 10.48550/arXiv.2401.01273.
10. Z. Wang, S. Xiao, J. Wang, A. Parab, and S. Patel, “Intelligent agricultural management considering N2O emission and climate variability with uncertainties,” Feb. 13, 2024. doi: 10.2139/ssrn.4742674.10.2139/ssrn.4742674 ↗
11. Overcoming catastrophic forgetting in neural networks10.1073/pnas.1611835114 ↗
12. G. M. van de Ven and A. S. Tolias, “Generative replay with feedback connections as a general strategy for continual learning,” Apr. 17, 2019. doi: 10.48550/arXiv.1809.10635.
13. Continual lifelong learning with neural networks: A review10.1016/j.neunet.2019.01.012 ↗
14. Lifelong machine learning: A paradigm for continuous learning10.1007/s11704-016-6903-6 ↗
15. J. Kim, Y. Ku, J. Kim, J. Cha, and S. Baek, “VLM-PL: Advanced pseudo labeling approach for class incremental object detection via vision-language model,” May 08, 2024. doi: 10.48550/arXiv.2403.05346.10.1109/CVPRW63382.2024.00420 ↗
16. Regularization theory and neural networks architectures10.1162/neco.1995.7.2.219 ↗
17. Continual learning through synaptic intelligence
18. Overcoming catastrophic forgetting by incremental moment matching
19. Learning without forgetting10.1109/TPAMI.2017.2773081 ↗
20. A. Gupta, V. Kumar, C. Lynch, S. Levine, and K. Hausman, “Relay policy learning: Solving long-Horizon tasks via imitation and reinforcement learning,” Oct. 25, 2019. doi: 10.48550/arXiv.1910.11956.
21. Self-improving reactive agents based on reinforcement learning, planning and teaching10.1007/BF00992699 ↗
22. Gradient episodic memory for continual learning
23. Continual learning of fault prediction for turbofan engines using deep learning with elastic weight consolidation
24. Continual learning strategy in one-stage object detection framework based on experience replay for autonomous driving vehicle10.3390/s20236777 ↗
25. Learning to navigate for mobile robot with continual reinforcement learning
26. A definition of continual reinforcement learning
27. Q-learning10.1007/BF00992698 ↗
28. K. Cho et al., “Learning phrase representations using RNN encoder-decoder for statistical machine translation,” Sep. 02, 2014. doi: 10.3115/v1/D14-1.10.3115/v1/D14-1179 ↗
29. V. Mnih et al., “Playing Atari with deep reinforcement learning,” Dec. 19, 2013. doi: 10.48550/arXiv.1312.5602.
30. J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” Aug. 28, 2017. doi: 10.48550/arXiv.1707.06347.
31. Z. Wang et al., “Sample efficient actor-critic with experience replay,” Jul. 10, 2017. doi: 10.48550/arXiv.1611.01224.
32. J. Thorne and A. Vlachos, “Elastic weight consolidation for better bias inoculation,” Feb. 4, 2021. doi: 10.18653/v1/2021.eacl-main.10.18653/v1/2021.eacl-main.82 ↗
33. A statistical comparison of the stochastic weather generators WGEN and SIMMETEO10.3354/cr024215 ↗
34. Reinforcement learning-based motion planning in partially observable environments under ethical constraints10.1007/s43681-024-00441-6 ↗
35. J. Li, M. Cai, Z. Kan, and S. Xiao, “Model-free motion planning of autonomous agents for complex tasks in partially observable environments,” Apr. 30, 2023. doi: 10.21203/rs.3.rs-2856026/v1.10.21203/rs.3.rs-2856026/v1 ↗